Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed. Here, the uncertainties in process and measurements are assumed non-Gaussian, such that the maximum correntropy criterion (MCC) is chosen to replace the conventional minimu...
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| Veröffentlicht in: | Control theory and technology 2022-11, Vol.20 (4), p.465-474 |
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| container_title | Control theory and technology |
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| creator | Dak, Aastha Radhakrishnan, Rahul |
| description | This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed. Here, the uncertainties in process and measurements are assumed non-Gaussian, such that the maximum correntropy criterion (MCC) is chosen to replace the conventional minimum mean square error criterion. Furthermore, the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function. Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems. |
| doi_str_mv | 10.1007/s11768-022-00116-9 |
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Here, the uncertainties in process and measurements are assumed non-Gaussian, such that the maximum correntropy criterion (MCC) is chosen to replace the conventional minimum mean square error criterion. Furthermore, the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function. Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.</description><identifier>ISSN: 2095-6983</identifier><identifier>EISSN: 2198-0942</identifier><identifier>DOI: 10.1007/s11768-022-00116-9</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Complexity ; Computational Intelligence ; Control ; Control and Systems Theory ; Cost function ; Criteria ; Engineering ; Kalman filters ; Kernels ; Mechatronics ; Optimization ; Research Article ; Robotics ; State estimation ; Systems Theory</subject><ispartof>Control theory and technology, 2022-11, Vol.20 (4), p.465-474</ispartof><rights>The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022. 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Here, the uncertainties in process and measurements are assumed non-Gaussian, such that the maximum correntropy criterion (MCC) is chosen to replace the conventional minimum mean square error criterion. Furthermore, the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function. Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.</description><subject>Complexity</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Control and Systems Theory</subject><subject>Cost function</subject><subject>Criteria</subject><subject>Engineering</subject><subject>Kalman filters</subject><subject>Kernels</subject><subject>Mechatronics</subject><subject>Optimization</subject><subject>Research Article</subject><subject>Robotics</subject><subject>State estimation</subject><subject>Systems Theory</subject><issn>2095-6983</issn><issn>2198-0942</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhosoOOb-gFcB74TqSdKP5FKGTnHojV6HrD2Z3dp0Jq1af72ZFXbnTRIOz_Me8kbROYUrCpBfe0rzTMTAWAxAaRbLo2jCqAwjmbDj8AaZxpkU_DSaeb-BQCU051xMInxqbVx16HRXfSCZ6754G8gWncU6XmmPJWn0V9X0DSla59B2rt0NpOhXuusdkkddN9oSU9Uhg5jWERsCF7r3vgpzP_gOG38WnRhde5z93dPo9e72ZX4fL58XD_ObZVwwkXTh1FyUCLAysqS8MGmSpCI1lKcATOeYFyWUTOjUSFylUoR_QAIFGpQJD8Y0uhxzP7U12q7Vpu2dDRvV9ruuh2FQyEJNwQEe4IsR3rn2vUffHWiWZyCyhOcyUGykCtd679Conasa7QZFQe3rV2P9KuSq3_rVXuKj5ANs1-gO0f9YP_MsiNk</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Dak, Aastha</creator><creator>Radhakrishnan, Rahul</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat 395007,India</general><scope>AAYXX</scope><scope>CITATION</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20221101</creationdate><title>Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems</title><author>Dak, Aastha ; Radhakrishnan, Rahul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c284t-c2a38de00bf9d13cf544585f135002a7e7cd0d28a5f9eb598014040cefe943d13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Complexity</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Control and Systems Theory</topic><topic>Cost function</topic><topic>Criteria</topic><topic>Engineering</topic><topic>Kalman filters</topic><topic>Kernels</topic><topic>Mechatronics</topic><topic>Optimization</topic><topic>Research Article</topic><topic>Robotics</topic><topic>State estimation</topic><topic>Systems Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dak, Aastha</creatorcontrib><creatorcontrib>Radhakrishnan, Rahul</creatorcontrib><collection>CrossRef</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Control theory and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dak, Aastha</au><au>Radhakrishnan, Rahul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems</atitle><jtitle>Control theory and technology</jtitle><stitle>Control Theory Technol</stitle><date>2022-11-01</date><risdate>2022</risdate><volume>20</volume><issue>4</issue><spage>465</spage><epage>474</epage><pages>465-474</pages><issn>2095-6983</issn><eissn>2198-0942</eissn><abstract>This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed. 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| ispartof | Control theory and technology, 2022-11, Vol.20 (4), p.465-474 |
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| source | SpringerNature Journals; Alma/SFX Local Collection |
| subjects | Complexity Computational Intelligence Control Control and Systems Theory Cost function Criteria Engineering Kalman filters Kernels Mechatronics Optimization Research Article Robotics State estimation Systems Theory |
| title | Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems |
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